To minimize Equation (REF ), we train the neural network by adjusting each neuron weight (\(\mathrm {W}\) ) and bias (\(\mathrm {b}\) ) using the adaptive moment estimation (ADAM) algorithm [1]}. Throughout this study, we use a batch size of 32, a learning rate of 0.001, a number of epoch of 10,000, and we normalize both our input and output to \([0, 1]\) . To prevent our networks from overfitting behavior, we follow early stopping and generalized cross-validation criteria [2]}, [3]}, [4]}. Note that instead of literally stopping our training cycle, we only save the set of trained weight and bias to be used in the online phase when the current validation loss is lower than the lowest validation from all the previous training cycle. We note that we do not use the RBF regression in linear approach because we follow procedures proposed in [2]}.
